Solid Oxide Fuel Cells (SOFCs) are emerging as a leading solution in sustainable power generation, boasting high power-to-energy density and minimal emissions. With efficiencies potentially exceeding 60% for electricity generation alone and up to 85% when in cogeneration applications, SOFCs significantly outperform traditional combustion-based technologies, which typically achieve efficiencies of around 35–40%. Operating effectively at elevated temperatures (600 °C to 1000 °C), SOFCs not only offer superior efficiency but also generate high-grade waste heat, making them ideal for cogeneration applications. However, these high operational temperatures pose significant thermal management challenges, necessitating innovative solutions to maintain system stability and longevity. This review aims to address these challenges by offering an exhaustive analysis of the latest advancements in SOFC thermal management. We begin by contextualizing the significance of thermal management in SOFC performance, focusing on its role in enhancing operational stability and minimizing thermal stresses. The core of this review delves into various thermal management subsystems such as afterburners, heat exchangers, and advanced thermal regulation strategies. A comprehensive examination of the recent literature is presented, highlighting innovations in subsystem design, fuel management, flow channel configuration, heat pipe integration, and efficient waste heat recovery techniques. In conclusion, we provide a forward-looking perspective on the state of research in SOFC thermal management, identifying potential avenues for future advancements and their implications for the broader field of sustainable energy technologies.

A solid oxide fuel cell (SOFC) system is a kind of green chemical-energy–electric-energy conversion equipment with broad application prospects. In order to ensure the long-term stable operation of the SOFC power-generation system, prediction and evaluation of the system’s operating state are required. The mechanism of the SOFC system has not been fully revealed, and data-driven single-step prediction is of little value for practical applications. The state-prediction problem can be regarded as a time series prediction problem. Therefore, an innovative deep learning model for SOFC system state prediction is proposed in this study. The model uses a two-layer LSTM network structure that supports multiple sequence feature inputs and flexible multi-step prediction outputs, which allows multi-step prediction of system states using SOFC system experimental data. Comparing the proposed model with the traditional ARIMA model and LSTM recursive prediction model, it is shown that the multi-step LSTM prediction model performs better than the ARIMA and LSTM recursive prediction models in terms of two evaluation criteria: root mean square error and mean absolute error. Thus, the proposed multi-step LSTM prediction model can effectively and accurately predict and evaluate the SOFC system’s state.

A solid oxide fuel cell (SOFC) is an innovative power generation system that is green, efficient, and promising for a wide range of applications. The prediction and evaluation of the operation state of a solid oxide fuel cell system is of great significance for the stable and long-term operation of the power generation system. Prognostics and Health Management (PHM) technology is widely used to perform preventive and predictive maintenance on equipment. Unlike prediction based on the SOFC mechanistic model, the combination of PHM and deep learning has shown wide application prospects. Therefore, this study first obtains an experimental dataset through short-term degradation experiments of a 1 kW SOFC system, and then proposes an encoder-decoder RNN-based SOFC state prediction model. Based on the experimental dataset, the model can accurately predict the voltage variation of the SOFC system. The prediction results of the four different prediction models developed are compared and analyzed, namely, long short-term memory (LSTM), gated recurrent unit (GRU), encoder–decoder LSTM, and encoder–decoder GRU. The results show that for the SOFC test set, the mean square error of encoder–decoder LSTM and encoder–decoder GRU are 0.015121 and 0.014966, respectively, whereas the corresponding error results of LSTM and GRU are 0.017050 and 0.017456, respectively. The encoder–decoder RNN model displays high prediction precision, which proves that it can improve the accuracy of prediction, which is expected to be combined with control strategies and further help the implementation of PHM in fuel cells.

In this study, a data-driven fault diagnosis method was developed for solid oxide fuel cell (SOFC) systems. First, the complete experimental data was obtained following the design of the SOFC system experiments. Then, principal component analysis (PCA) was performed to reduce the dimensionality of the obtained experimental data. Finally, the fault diagnosis algorithms were designed by support vector machine (SVM) and BP neural network to identify and prevent the reformer carbon deposition and heat exchanger rupture faults, respectively. The research results show that both SVM and BP fault diagnosis algorithms can achieve online fault identification. The PCA + SVM algorithm was compared with the SVM algorithm, BP algorithm, and PCA + BP algorithm, and the results show that the PCA + SVM algorithm is superior in terms of running time and accuracy, the diagnosis accuracy reached more than 99%, and the running time was within 20 s. The corresponding system optimization scheme is also proposed.

Direct ethanol solid oxide fuel cells (SOFCs) are the most energy-efficient and low-carbon technology for renewable power generation from biomass fuels, but they are hindered by carbon deposition on the Ni-based cermet anode. In this work, excessive Na⁺ dopant into La_0.75Sr_0.25Cr_0.5Fe_0.4Cu_0.1O_3-δ (LSCFC) perovskite was used as an additional internal reforming catalyst for direct carbon dioxide-ethanol SOFCs. Excessive Na⁺-doped LSCFC (N-LSCFC) demonstrated great potential in promoting electrochemical performance and internal reforming process fueled by carbon dioxide-ethanol mixture, because more oxygen vacancies and the precipitated Cu nano catalyst were helpful for the improvement of internal reforming and carbon tolerance. Electrochemical investigations proved that the vertical-microchannel anode supported the single cells using the N-LSCFC-Gd_0.1Ce_0.9O_2-δ (GDC) internal reforming catalyst, showing a peak power density of 1044.41 and 855.56 mW/cm² at 800 °C fueled by H₂ and 50% CO₂-50% C₂H₅OH, respectively. The preceding results indicate that excessive Na⁺ doping strategy into LSCFC as the additional internal reforming catalyst can improve the electrochemical performance and internal reforming process of direct carbon dioxide-ethanol SOFCs.

Microalgae are known for their efficient removal of hexavalent chromium (Cr(VI)) through biosorption and bioaccumulation, yet the subsequent release of Cr(VI) upon cell death remains a challenge. The reduction of Cr(VI) to the less toxic trivalent chromium [Cr(III)] is another critical remediation strategy that mitigates the risk of Cr(VI) re-release, but research on microalgal reduction of Cr(VI) is scarce. In this study, a microalgal strain designated as MQ-1 was isolated from chromium-contaminated mine effluent, demonstrating the capability to tolerate and remove Cr(VI). Phylogenetic analysis revealed that MQ-1 is closely related to the genus Chlorella; hence, it is classified as Chlorella sp. MQ-1. This strain exhibited robust growth at Cr(VI) concentrations below 2 mg/L, achieving a removal rate higher than 82% for initial Cr(VI) concentrations between 0.5 and 1 mg/L after a 5-day incubation period. Mechanistic studies revealed that MQ-1 promoted the removal of Cr(VI) mainly through intracellular bioreduction and bioaccumulation processes, in which more than 60% of Cr(VI) was reduced to the less toxic Cr(III) and stocked in the cells. A two-stage cultivation strategy, involving initial biomass accumulation followed by Cr(VI) treatment, significantly enhanced the removal efficiency, which was further accelerated under illuminated conditions. Notably, MQ-1 cultures with initial OD₆₈₀ values of 4 and 6 accomplished 84.28% and 91.31% Cr(VI) removal from 2 mg/L solutions, respectively, within 30 hours under light exposure. These findings highlight the potential of MQ-1 to utilize renewable solar energy to reduce Cr(VI) and to mitigate the risk of its re-release into the environment. This characteristic positions MQ-1 as a potentially sustainable and cost-effective solution for Cr(VI) remediation and suggests its significant potential for large-scale implementation in bioremediation strategies aimed at Cr(VI)-contaminated waters.